It is desirable in many instances to match or otherwise correlate a test image with a template. For example, the test image may be an image which was recently obtained or captured, while the template may be based upon a reference image obtained at some prior time. By matching the test image to the template, specific objects or targets which are designated in the template can be identified within the test image. This ability to identify specific objects within the test image which have previously been designated within a template is quite useful in a number of applications, including robotic control and other machine vision applications, automated inspection systems, automated surveillance systems and medical imagery applications. More specifically, in missile guidance applications, it is particularly desirable to automatically identify a target within a test image which has previously been designated in a template or a reconnaissance photograph.
A number of image correlation and image detection methods have been developed to identify previously designated objects within a test image. For example, a number of image correlation systems have been developed which detect edges or other boundaries within the test image. As known to those skilled in the art, edges or boundaries of objects tend to create intensity discontinuities within the test image. Accordingly, by identifying the edges or boundaries of objects, an outline of the objects can sometimes be created. Thereafter, the edge detection systems generally attempt to recognize previously designated objects based upon their spatial relationship to an edge or other boundary which has been identified within the test image.
Another common image detection method is the Hough method. The Hough method is particularly applicable if little, if any, information is known regarding the relative location of an edge or other boundary within the test image, but if the shape of the designated object can be described as a parametric curve since the Hough method is well suited to detect such curves. The Hough method, as well as other image detection methods, are described in a book entitled Computer Vision by Ballard, et al., published by Prentice-Hall, Inc., Englewood Cliffs, N.J. (1982).
Although extensive research has been conducted to efficiently implement image detection systems, such as edge detection systems and systems based upon the Hough method as described above and in more detail in the book entitled Computer Vision, these conventional image detection and correlation systems still suffer from several deficiencies. For example, many conventional image detection methods require significant amounts of data processing and computation in order to effectively compare the test image to the template or reference image. Thus, image detection systems based upon these conventional detection methods may require a significant amount of time in order to compare the test image to the template and to identify the designated object within the test image. While the delays engendered by these lengthy comparison processes are acceptable in many applications, some applications, such as robotic control or other machine vision applications and missile guidance applications, typically require a relatively rapid comparison of the test image to the template and, in some instances, may demand a near real time comparison of the test image to the template in order to effectively identify the designated object or target.
In addition, at least some of these conventional image detection systems require the test image as well as the reference image from which the template is created to be obtained under relatively similar lighting and other environmental conditions in order to properly compare the test image to the template. These environmental conditions include, among other things, the viewpoint, i.e., the angle and direction, from which the template and the test image were obtained. If the lighting or other environmental conditions change between the time at which the reference image from which the template is created is obtained and the time at which the test image is obtained, at least some of these conventional image detection methods require that the image depicted by the template be modeled.
For example, in missile guidance applications in which the template and the test image are images of the terrain or landscape at a predetermined location, conventional image detection methods generally require the viewpoint from which the reference image and the test image are obtained to closely correspond in order to properly identify a designated object within the test image. If, however, the lighting or environmental conditions, such as the respective viewpoints, change between the time at which the reference image is obtained and the time at which the test image is obtained, the terrain at the desired location must generally be modeled from the reference image according to a manual mensuration procession. As known to those skilled in the art, the terrain at the desired location can be mensurated by constructing three-dimensional models of the landscape, including three-dimensional models of any buildings of interest, at the desired location. As also known to those skilled in the art, the construction of appropriate three-dimensional models generally requires the craftsmanship of an expert who is skilled and experienced in the mensuration process. Even with the assistance of a mensuration expert, however, the construction of the appropriate three-dimensional structures can consume hours or sometimes even days.
Once the terrain has been modeled based upon the reference image, the template can be created based upon the model in order to compensate for the variations in the lighting and other environmental conditions between the time at which the reference image was obtained and the time at which the test image is obtained. In this fashion, these conventional image detection systems can compare the template to the test image.
Conventional image detection systems also typically require the reference image and the test image to be obtained by sources of the same spectral type. For example, conventional image detection systems may require both the reference and test images to be obtained by visible wavelength sensors, such as photographic systems. Alternatively, conventional image detection systems may require both the reference and test images to be obtained by infrared sensors or by synthetic aperture radar. In any instance, conventional image detection systems generally do not permit the reference and test images to be obtained by different types or cross-spectral sources. In addition, conventional image detection systems may restrict the types, shapes, locations or materials of the objects which can be reliably designated and identified due, at least in part, to limitations imposed upon the types of sensors which can be employed to create the reference and test images.